{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e85f3e56",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.regression.rolling import RollingOLS\n",
    "from tqdm.notebook import tqdm\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4316a07b",
   "metadata": {},
   "outputs": [],
   "source": [
    "factor_folder = 'factors/'\n",
    "\n",
    "index_ret = pd.read_csv(factor_folder+'index_ret.csv',index_col=0)\n",
    "close_ret = pd.read_csv(factor_folder+'close_ret.csv',index_col=0).shift(-1)\n",
    "BP = pd.read_csv(factor_folder+'BP.csv',index_col=0)\n",
    "SIZE = pd.read_csv(factor_folder+'SIZE.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5c18c016",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>000001.SZ</th>\n",
       "      <th>000002.SZ</th>\n",
       "      <th>000004.SZ</th>\n",
       "      <th>000005.SZ</th>\n",
       "      <th>000006.SZ</th>\n",
       "      <th>000007.SZ</th>\n",
       "      <th>000008.SZ</th>\n",
       "      <th>000009.SZ</th>\n",
       "      <th>000010.SZ</th>\n",
       "      <th>000011.SZ</th>\n",
       "      <th>...</th>\n",
       "      <th>603989.SH</th>\n",
       "      <th>603990.SH</th>\n",
       "      <th>603991.SH</th>\n",
       "      <th>603992.SH</th>\n",
       "      <th>603993.SH</th>\n",
       "      <th>603995.SH</th>\n",
       "      <th>603996.SH</th>\n",
       "      <th>603997.SH</th>\n",
       "      <th>603998.SH</th>\n",
       "      <th>603999.SH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>2010-01-04</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
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       "      <td>-0.031319</td>\n",
       "      <td>-0.008595</td>\n",
       "      <td>-0.011774</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.011774</td>\n",
       "      <td>-0.001483</td>\n",
       "      <td>-0.008247</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>-0.002388</td>\n",
       "      <td>0.001439</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.033431</td>\n",
       "      <td>0.002187</td>\n",
       "      <td>-0.021107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.027975</td>\n",
       "      <td>-0.029709</td>\n",
       "      <td>-0.015198</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>0.016305</td>\n",
       "      <td>0.026178</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.029299</td>\n",
       "      <td>0.041178</td>\n",
       "      <td>0.027626</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.064997</td>\n",
       "      <td>0.036635</td>\n",
       "      <td>0.028630</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>-0.001008</td>\n",
       "      <td>-0.018176</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.012964</td>\n",
       "      <td>-0.026326</td>\n",
       "      <td>0.003247</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.019546</td>\n",
       "      <td>0.005936</td>\n",
       "      <td>0.024845</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-02</th>\n",
       "      <td>0.002737</td>\n",
       "      <td>0.033998</td>\n",
       "      <td>0.031554</td>\n",
       "      <td>0.020637</td>\n",
       "      <td>0.020778</td>\n",
       "      <td>0.059300</td>\n",
       "      <td>0.006338</td>\n",
       "      <td>-0.021497</td>\n",
       "      <td>0.018684</td>\n",
       "      <td>0.017698</td>\n",
       "      <td>...</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.013060</td>\n",
       "      <td>0.014061</td>\n",
       "      <td>0.028459</td>\n",
       "      <td>0.021243</td>\n",
       "      <td>0.017281</td>\n",
       "      <td>0.004961</td>\n",
       "      <td>0.011974</td>\n",
       "      <td>0.016774</td>\n",
       "      <td>0.021493</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-03</th>\n",
       "      <td>-0.006889</td>\n",
       "      <td>0.002579</td>\n",
       "      <td>0.006200</td>\n",
       "      <td>0.002025</td>\n",
       "      <td>0.002025</td>\n",
       "      <td>0.048079</td>\n",
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       "      <td>0.034544</td>\n",
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       "      <td>0.003935</td>\n",
       "      <td>0.006183</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-04</th>\n",
       "      <td>-0.021019</td>\n",
       "      <td>-0.021443</td>\n",
       "      <td>0.014302</td>\n",
       "      <td>-0.012861</td>\n",
       "      <td>-0.015373</td>\n",
       "      <td>-0.035844</td>\n",
       "      <td>-0.008065</td>\n",
       "      <td>-0.076369</td>\n",
       "      <td>-0.023529</td>\n",
       "      <td>-0.024310</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009040</td>\n",
       "      <td>-0.017663</td>\n",
       "      <td>-0.003697</td>\n",
       "      <td>-0.019161</td>\n",
       "      <td>-0.040326</td>\n",
       "      <td>0.038055</td>\n",
       "      <td>0.000798</td>\n",
       "      <td>0.017601</td>\n",
       "      <td>-0.009975</td>\n",
       "      <td>0.006354</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2878 rows × 1933 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            000001.SZ  000002.SZ  000004.SZ  000005.SZ  000006.SZ  000007.SZ  \\\n",
       "trade_date                                                                     \n",
       "2010-01-04        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-05  -0.029307  -0.011774        NaN  -0.031319  -0.008595  -0.011774   \n",
       "2010-01-06  -0.002388   0.001439        NaN  -0.033431   0.002187  -0.021107   \n",
       "2010-01-07   0.016305   0.026178        NaN   0.029299   0.041178   0.027626   \n",
       "2010-01-08  -0.001008  -0.018176        NaN  -0.012964  -0.026326   0.003247   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "2021-11-02   0.002737   0.033998   0.031554   0.020637   0.020778   0.059300   \n",
       "2021-11-03  -0.006889   0.002579   0.006200   0.002025   0.002025   0.048079   \n",
       "2021-11-04  -0.021019  -0.021443   0.014302  -0.012861  -0.015373  -0.035844   \n",
       "2021-11-05        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2021-11-08        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "            000008.SZ  000009.SZ  000010.SZ  000011.SZ  ...  603989.SH  \\\n",
       "trade_date                                              ...              \n",
       "2010-01-04        NaN        NaN        NaN        NaN  ...        NaN   \n",
       "2010-01-05        NaN  -0.011774  -0.001483  -0.008247  ...        NaN   \n",
       "2010-01-06        NaN   0.027975  -0.029709  -0.015198  ...        NaN   \n",
       "2010-01-07        NaN   0.064997   0.036635   0.028630  ...        NaN   \n",
       "2010-01-08        NaN  -0.019546   0.005936   0.024845  ...        NaN   \n",
       "...               ...        ...        ...        ...  ...        ...   \n",
       "2021-11-02   0.006338  -0.021497   0.018684   0.017698  ...   0.035714   \n",
       "2021-11-03  -0.007409   0.034544  -0.003077   0.004873  ...   0.097311   \n",
       "2021-11-04  -0.008065  -0.076369  -0.023529  -0.024310  ...  -0.009040   \n",
       "2021-11-05        NaN        NaN        NaN        NaN  ...        NaN   \n",
       "2021-11-08        NaN        NaN        NaN        NaN  ...        NaN   \n",
       "\n",
       "            603990.SH  603991.SH  603992.SH  603993.SH  603995.SH  603996.SH  \\\n",
       "trade_date                                                                     \n",
       "2010-01-04        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-05        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-06        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-07        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-08        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "2021-11-02   0.013060   0.014061   0.028459   0.021243   0.017281   0.004961   \n",
       "2021-11-03   0.014674   0.015243  -0.001123  -0.037738   0.025098   0.026050   \n",
       "2021-11-04  -0.017663  -0.003697  -0.019161  -0.040326   0.038055   0.000798   \n",
       "2021-11-05        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2021-11-08        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "            603997.SH  603998.SH  603999.SH  \n",
       "trade_date                                   \n",
       "2010-01-04        NaN        NaN        NaN  \n",
       "2010-01-05        NaN        NaN        NaN  \n",
       "2010-01-06        NaN        NaN        NaN  \n",
       "2010-01-07        NaN        NaN        NaN  \n",
       "2010-01-08        NaN        NaN        NaN  \n",
       "...               ...        ...        ...  \n",
       "2021-11-02   0.011974   0.016774   0.021493  \n",
       "2021-11-03   0.027414   0.003935   0.006183  \n",
       "2021-11-04   0.017601  -0.009975   0.006354  \n",
       "2021-11-05        NaN        NaN        NaN  \n",
       "2021-11-08        NaN        NaN        NaN  \n",
       "\n",
       "[2878 rows x 1933 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_ret.subtract(index_ret['close'],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "ddbb7adf",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>000001.SZ</th>\n",
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       "      <td>0.034458</td>\n",
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       "      <td>-0.027929</td>\n",
       "      <td>-0.017094</td>\n",
       "      <td>-0.031307</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>-0.017532</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.019545</td>\n",
       "      <td>0.003180</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010292</td>\n",
       "      <td>0.003527</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>-0.010944</td>\n",
       "      <td>-0.007117</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.041988</td>\n",
       "      <td>-0.006369</td>\n",
       "      <td>-0.029663</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.019418</td>\n",
       "      <td>-0.038266</td>\n",
       "      <td>-0.023754</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>-0.002755</td>\n",
       "      <td>0.007117</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010239</td>\n",
       "      <td>0.022118</td>\n",
       "      <td>0.008565</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.045937</td>\n",
       "      <td>0.017575</td>\n",
       "      <td>0.009569</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.017168</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.011956</td>\n",
       "      <td>-0.025318</td>\n",
       "      <td>0.004255</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.018538</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>0.025853</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-02</th>\n",
       "      <td>-0.008285</td>\n",
       "      <td>0.022977</td>\n",
       "      <td>0.020532</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0.009756</td>\n",
       "      <td>0.048278</td>\n",
       "      <td>-0.004684</td>\n",
       "      <td>-0.032519</td>\n",
       "      <td>0.007663</td>\n",
       "      <td>0.006676</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024693</td>\n",
       "      <td>0.002039</td>\n",
       "      <td>0.003039</td>\n",
       "      <td>0.017438</td>\n",
       "      <td>0.010222</td>\n",
       "      <td>0.006259</td>\n",
       "      <td>-0.006061</td>\n",
       "      <td>0.000953</td>\n",
       "      <td>0.005753</td>\n",
       "      <td>0.010471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-03</th>\n",
       "      <td>-0.008914</td>\n",
       "      <td>0.000554</td>\n",
       "      <td>0.004175</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.046054</td>\n",
       "      <td>-0.009434</td>\n",
       "      <td>0.032519</td>\n",
       "      <td>-0.005102</td>\n",
       "      <td>0.002848</td>\n",
       "      <td>...</td>\n",
       "      <td>0.095286</td>\n",
       "      <td>0.012649</td>\n",
       "      <td>0.013218</td>\n",
       "      <td>-0.003148</td>\n",
       "      <td>-0.039763</td>\n",
       "      <td>0.023074</td>\n",
       "      <td>0.024025</td>\n",
       "      <td>0.025389</td>\n",
       "      <td>0.001910</td>\n",
       "      <td>0.004158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-04</th>\n",
       "      <td>-0.012954</td>\n",
       "      <td>-0.013378</td>\n",
       "      <td>0.022367</td>\n",
       "      <td>-0.004796</td>\n",
       "      <td>-0.007308</td>\n",
       "      <td>-0.027780</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.068304</td>\n",
       "      <td>-0.015464</td>\n",
       "      <td>-0.016245</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000975</td>\n",
       "      <td>-0.009598</td>\n",
       "      <td>0.004367</td>\n",
       "      <td>-0.011097</td>\n",
       "      <td>-0.032261</td>\n",
       "      <td>0.046120</td>\n",
       "      <td>0.008863</td>\n",
       "      <td>0.025666</td>\n",
       "      <td>-0.001910</td>\n",
       "      <td>0.014418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2878 rows × 1933 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            000001.SZ  000002.SZ  000004.SZ  000005.SZ  000006.SZ  000007.SZ  \\\n",
       "trade_date                                                                     \n",
       "2010-01-04  -0.017230  -0.023827        NaN   0.034458  -0.031351   0.036136   \n",
       "2010-01-05  -0.017532   0.000000        NaN  -0.019545   0.003180   0.000000   \n",
       "2010-01-06  -0.010944  -0.007117        NaN  -0.041988  -0.006369  -0.029663   \n",
       "2010-01-07  -0.002755   0.007117        NaN   0.010239   0.022118   0.008565   \n",
       "2010-01-08   0.000000  -0.017168        NaN  -0.011956  -0.025318   0.004255   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "2021-11-02  -0.008285   0.022977   0.020532   0.009615   0.009756   0.048278   \n",
       "2021-11-03  -0.008914   0.000554   0.004175   0.000000   0.000000   0.046054   \n",
       "2021-11-04  -0.012954  -0.013378   0.022367  -0.004796  -0.007308  -0.027780   \n",
       "2021-11-05        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2021-11-08        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "            000008.SZ  000009.SZ  000010.SZ  000011.SZ  ...  603989.SH  \\\n",
       "trade_date                                              ...              \n",
       "2010-01-04        NaN  -0.027929  -0.017094  -0.031307  ...        NaN   \n",
       "2010-01-05        NaN   0.000000   0.010292   0.003527  ...        NaN   \n",
       "2010-01-06        NaN   0.019418  -0.038266  -0.023754  ...        NaN   \n",
       "2010-01-07        NaN   0.045937   0.017575   0.009569  ...        NaN   \n",
       "2010-01-08        NaN  -0.018538   0.006944   0.025853  ...        NaN   \n",
       "...               ...        ...        ...        ...  ...        ...   \n",
       "2021-11-02  -0.004684  -0.032519   0.007663   0.006676  ...   0.024693   \n",
       "2021-11-03  -0.009434   0.032519  -0.005102   0.002848  ...   0.095286   \n",
       "2021-11-04   0.000000  -0.068304  -0.015464  -0.016245  ...  -0.000975   \n",
       "2021-11-05        NaN        NaN        NaN        NaN  ...        NaN   \n",
       "2021-11-08        NaN        NaN        NaN        NaN  ...        NaN   \n",
       "\n",
       "            603990.SH  603991.SH  603992.SH  603993.SH  603995.SH  603996.SH  \\\n",
       "trade_date                                                                     \n",
       "2010-01-04        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-05        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-06        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-07        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-08        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "2021-11-02   0.002039   0.003039   0.017438   0.010222   0.006259  -0.006061   \n",
       "2021-11-03   0.012649   0.013218  -0.003148  -0.039763   0.023074   0.024025   \n",
       "2021-11-04  -0.009598   0.004367  -0.011097  -0.032261   0.046120   0.008863   \n",
       "2021-11-05        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2021-11-08        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "            603997.SH  603998.SH  603999.SH  \n",
       "trade_date                                   \n",
       "2010-01-04        NaN        NaN        NaN  \n",
       "2010-01-05        NaN        NaN        NaN  \n",
       "2010-01-06        NaN        NaN        NaN  \n",
       "2010-01-07        NaN        NaN        NaN  \n",
       "2010-01-08        NaN        NaN        NaN  \n",
       "...               ...        ...        ...  \n",
       "2021-11-02   0.000953   0.005753   0.010471  \n",
       "2021-11-03   0.025389   0.001910   0.004158  \n",
       "2021-11-04   0.025666  -0.001910   0.014418  \n",
       "2021-11-05        NaN        NaN        NaN  \n",
       "2021-11-08        NaN        NaN        NaN  \n",
       "\n",
       "[2878 rows x 1933 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "331aa3ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trade_date\n",
       "2010-01-04         NaN\n",
       "2010-01-05    0.011774\n",
       "2010-01-06   -0.008556\n",
       "2010-01-07   -0.019060\n",
       "2010-01-08    0.001008\n",
       "                ...   \n",
       "2021-11-02   -0.011022\n",
       "2021-11-03   -0.002025\n",
       "2021-11-04    0.008065\n",
       "2021-11-05   -0.010059\n",
       "2021-11-08    0.002021\n",
       "Name: close, Length: 2878, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_ret['close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bde8dcc2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8951035",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e611f42e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c7b000f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>000001.SZ</th>\n",
       "      <th>000002.SZ</th>\n",
       "      <th>000004.SZ</th>\n",
       "      <th>000005.SZ</th>\n",
       "      <th>000006.SZ</th>\n",
       "      <th>000007.SZ</th>\n",
       "      <th>000008.SZ</th>\n",
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       "      <th>000010.SZ</th>\n",
       "      <th>000011.SZ</th>\n",
       "      <th>...</th>\n",
       "      <th>603989.SH</th>\n",
       "      <th>603990.SH</th>\n",
       "      <th>603991.SH</th>\n",
       "      <th>603992.SH</th>\n",
       "      <th>603993.SH</th>\n",
       "      <th>603995.SH</th>\n",
       "      <th>603996.SH</th>\n",
       "      <th>603997.SH</th>\n",
       "      <th>603998.SH</th>\n",
       "      <th>603999.SH</th>\n",
       "    </tr>\n",
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       "      <th>trade_date</th>\n",
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       "      <th>2010-01-04</th>\n",
       "      <td>7.362983e+06</td>\n",
       "      <td>1.165492e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>547685.8306</td>\n",
       "      <td>563987.7873</td>\n",
       "      <td>128180.9966</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.182374e+06</td>\n",
       "      <td>173627.6061</td>\n",
       "      <td>634121.7539</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>7.235661e+06</td>\n",
       "      <td>1.139104e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>566886.8363</td>\n",
       "      <td>547757.9230</td>\n",
       "      <td>132990.0960</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.151833e+06</td>\n",
       "      <td>170540.2397</td>\n",
       "      <td>614454.4439</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>7.111443e+06</td>\n",
       "      <td>1.139104e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>555914.8331</td>\n",
       "      <td>549279.4727</td>\n",
       "      <td>132990.0960</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.150742e+06</td>\n",
       "      <td>172304.4491</td>\n",
       "      <td>616242.3811</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>7.033807e+06</td>\n",
       "      <td>1.130308e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>533056.4929</td>\n",
       "      <td>544714.8234</td>\n",
       "      <td>129105.8234</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.172557e+06</td>\n",
       "      <td>165541.6464</td>\n",
       "      <td>601938.8829</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>7.018280e+06</td>\n",
       "      <td>1.138004e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>538542.4945</td>\n",
       "      <td>557901.5882</td>\n",
       "      <td>130030.6502</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.229276e+06</td>\n",
       "      <td>168481.9954</td>\n",
       "      <td>607898.6738</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-01</th>\n",
       "      <td>3.762808e+07</td>\n",
       "      <td>2.114657e+07</td>\n",
       "      <td>262709.0958</td>\n",
       "      <td>228643.9579</td>\n",
       "      <td>561597.9391</td>\n",
       "      <td>159366.1002</td>\n",
       "      <td>603432.5901</td>\n",
       "      <td>5.287389e+06</td>\n",
       "      <td>329581.5946</td>\n",
       "      <td>634717.7330</td>\n",
       "      <td>...</td>\n",
       "      <td>1.464620e+06</td>\n",
       "      <td>333905.3188</td>\n",
       "      <td>325867.0113</td>\n",
       "      <td>755903.5823</td>\n",
       "      <td>1.295954e+07</td>\n",
       "      <td>1.002215e+06</td>\n",
       "      <td>103251.60</td>\n",
       "      <td>1.172272e+06</td>\n",
       "      <td>225450.6030</td>\n",
       "      <td>279360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-02</th>\n",
       "      <td>3.527996e+07</td>\n",
       "      <td>2.050718e+07</td>\n",
       "      <td>255688.9596</td>\n",
       "      <td>219117.1263</td>\n",
       "      <td>550797.9788</td>\n",
       "      <td>161098.3405</td>\n",
       "      <td>595090.2040</td>\n",
       "      <td>5.078472e+06</td>\n",
       "      <td>319743.3381</td>\n",
       "      <td>622798.1511</td>\n",
       "      <td>...</td>\n",
       "      <td>1.454232e+06</td>\n",
       "      <td>324308.4365</td>\n",
       "      <td>318338.9765</td>\n",
       "      <td>752294.4936</td>\n",
       "      <td>1.261396e+07</td>\n",
       "      <td>1.002448e+06</td>\n",
       "      <td>99349.65</td>\n",
       "      <td>1.152496e+06</td>\n",
       "      <td>223303.4544</td>\n",
       "      <td>273600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-03</th>\n",
       "      <td>3.498887e+07</td>\n",
       "      <td>2.098382e+07</td>\n",
       "      <td>260993.0625</td>\n",
       "      <td>221234.2000</td>\n",
       "      <td>556197.9590</td>\n",
       "      <td>169066.6455</td>\n",
       "      <td>592309.4087</td>\n",
       "      <td>4.915982e+06</td>\n",
       "      <td>322202.9022</td>\n",
       "      <td>626970.0048</td>\n",
       "      <td>...</td>\n",
       "      <td>1.490588e+06</td>\n",
       "      <td>324970.2904</td>\n",
       "      <td>319307.9314</td>\n",
       "      <td>765527.8189</td>\n",
       "      <td>1.274355e+07</td>\n",
       "      <td>1.008712e+06</td>\n",
       "      <td>98749.35</td>\n",
       "      <td>1.153595e+06</td>\n",
       "      <td>224591.7436</td>\n",
       "      <td>276480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-04</th>\n",
       "      <td>3.467838e+07</td>\n",
       "      <td>2.099544e+07</td>\n",
       "      <td>262085.0837</td>\n",
       "      <td>221234.2000</td>\n",
       "      <td>556197.9590</td>\n",
       "      <td>177034.9505</td>\n",
       "      <td>586747.8180</td>\n",
       "      <td>5.078472e+06</td>\n",
       "      <td>320563.1928</td>\n",
       "      <td>628757.9421</td>\n",
       "      <td>...</td>\n",
       "      <td>1.639607e+06</td>\n",
       "      <td>329106.8776</td>\n",
       "      <td>323556.4263</td>\n",
       "      <td>763121.7598</td>\n",
       "      <td>1.224677e+07</td>\n",
       "      <td>1.032257e+06</td>\n",
       "      <td>101150.55</td>\n",
       "      <td>1.183259e+06</td>\n",
       "      <td>225021.1733</td>\n",
       "      <td>277632.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-05</th>\n",
       "      <td>3.423204e+07</td>\n",
       "      <td>2.071643e+07</td>\n",
       "      <td>268013.1987</td>\n",
       "      <td>220175.6631</td>\n",
       "      <td>552147.9738</td>\n",
       "      <td>172184.6779</td>\n",
       "      <td>586747.8180</td>\n",
       "      <td>4.743174e+06</td>\n",
       "      <td>315644.0645</td>\n",
       "      <td>618626.2975</td>\n",
       "      <td>...</td>\n",
       "      <td>1.638009e+06</td>\n",
       "      <td>325963.0714</td>\n",
       "      <td>324972.5913</td>\n",
       "      <td>754700.5528</td>\n",
       "      <td>1.185798e+07</td>\n",
       "      <td>1.080979e+06</td>\n",
       "      <td>102051.00</td>\n",
       "      <td>1.214021e+06</td>\n",
       "      <td>224591.7436</td>\n",
       "      <td>281664.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2877 rows × 1933 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               000001.SZ     000002.SZ    000004.SZ    000005.SZ    000006.SZ  \\\n",
       "trade_date                                                                      \n",
       "2010-01-04  7.362983e+06  1.165492e+07          NaN  547685.8306  563987.7873   \n",
       "2010-01-05  7.235661e+06  1.139104e+07          NaN  566886.8363  547757.9230   \n",
       "2010-01-06  7.111443e+06  1.139104e+07          NaN  555914.8331  549279.4727   \n",
       "2010-01-07  7.033807e+06  1.130308e+07          NaN  533056.4929  544714.8234   \n",
       "2010-01-08  7.018280e+06  1.138004e+07          NaN  538542.4945  557901.5882   \n",
       "...                  ...           ...          ...          ...          ...   \n",
       "2021-11-01  3.762808e+07  2.114657e+07  262709.0958  228643.9579  561597.9391   \n",
       "2021-11-02  3.527996e+07  2.050718e+07  255688.9596  219117.1263  550797.9788   \n",
       "2021-11-03  3.498887e+07  2.098382e+07  260993.0625  221234.2000  556197.9590   \n",
       "2021-11-04  3.467838e+07  2.099544e+07  262085.0837  221234.2000  556197.9590   \n",
       "2021-11-05  3.423204e+07  2.071643e+07  268013.1987  220175.6631  552147.9738   \n",
       "\n",
       "              000007.SZ    000008.SZ     000009.SZ    000010.SZ    000011.SZ  \\\n",
       "trade_date                                                                     \n",
       "2010-01-04  128180.9966          NaN  1.182374e+06  173627.6061  634121.7539   \n",
       "2010-01-05  132990.0960          NaN  1.151833e+06  170540.2397  614454.4439   \n",
       "2010-01-06  132990.0960          NaN  1.150742e+06  172304.4491  616242.3811   \n",
       "2010-01-07  129105.8234          NaN  1.172557e+06  165541.6464  601938.8829   \n",
       "2010-01-08  130030.6502          NaN  1.229276e+06  168481.9954  607898.6738   \n",
       "...                 ...          ...           ...          ...          ...   \n",
       "2021-11-01  159366.1002  603432.5901  5.287389e+06  329581.5946  634717.7330   \n",
       "2021-11-02  161098.3405  595090.2040  5.078472e+06  319743.3381  622798.1511   \n",
       "2021-11-03  169066.6455  592309.4087  4.915982e+06  322202.9022  626970.0048   \n",
       "2021-11-04  177034.9505  586747.8180  5.078472e+06  320563.1928  628757.9421   \n",
       "2021-11-05  172184.6779  586747.8180  4.743174e+06  315644.0645  618626.2975   \n",
       "\n",
       "            ...     603989.SH    603990.SH    603991.SH    603992.SH  \\\n",
       "trade_date  ...                                                        \n",
       "2010-01-04  ...           NaN          NaN          NaN          NaN   \n",
       "2010-01-05  ...           NaN          NaN          NaN          NaN   \n",
       "2010-01-06  ...           NaN          NaN          NaN          NaN   \n",
       "2010-01-07  ...           NaN          NaN          NaN          NaN   \n",
       "2010-01-08  ...           NaN          NaN          NaN          NaN   \n",
       "...         ...           ...          ...          ...          ...   \n",
       "2021-11-01  ...  1.464620e+06  333905.3188  325867.0113  755903.5823   \n",
       "2021-11-02  ...  1.454232e+06  324308.4365  318338.9765  752294.4936   \n",
       "2021-11-03  ...  1.490588e+06  324970.2904  319307.9314  765527.8189   \n",
       "2021-11-04  ...  1.639607e+06  329106.8776  323556.4263  763121.7598   \n",
       "2021-11-05  ...  1.638009e+06  325963.0714  324972.5913  754700.5528   \n",
       "\n",
       "               603993.SH     603995.SH  603996.SH     603997.SH    603998.SH  \\\n",
       "trade_date                                                                     \n",
       "2010-01-04           NaN           NaN        NaN           NaN          NaN   \n",
       "2010-01-05           NaN           NaN        NaN           NaN          NaN   \n",
       "2010-01-06           NaN           NaN        NaN           NaN          NaN   \n",
       "2010-01-07           NaN           NaN        NaN           NaN          NaN   \n",
       "2010-01-08           NaN           NaN        NaN           NaN          NaN   \n",
       "...                  ...           ...        ...           ...          ...   \n",
       "2021-11-01  1.295954e+07  1.002215e+06  103251.60  1.172272e+06  225450.6030   \n",
       "2021-11-02  1.261396e+07  1.002448e+06   99349.65  1.152496e+06  223303.4544   \n",
       "2021-11-03  1.274355e+07  1.008712e+06   98749.35  1.153595e+06  224591.7436   \n",
       "2021-11-04  1.224677e+07  1.032257e+06  101150.55  1.183259e+06  225021.1733   \n",
       "2021-11-05  1.185798e+07  1.080979e+06  102051.00  1.214021e+06  224591.7436   \n",
       "\n",
       "            603999.SH  \n",
       "trade_date             \n",
       "2010-01-04        NaN  \n",
       "2010-01-05        NaN  \n",
       "2010-01-06        NaN  \n",
       "2010-01-07        NaN  \n",
       "2010-01-08        NaN  \n",
       "...               ...  \n",
       "2021-11-01   279360.0  \n",
       "2021-11-02   273600.0  \n",
       "2021-11-03   276480.0  \n",
       "2021-11-04   277632.0  \n",
       "2021-11-05   281664.0  \n",
       "\n",
       "[2877 rows x 1933 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e560ce81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6e1009bb255d42a3b648bde750a842c7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1933 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dds = []\n",
    "for code in tqdm(close_ret.columns):\n",
    "    y = close_ret[code]\n",
    "    X = index_ret\n",
    "    X = sm.add_constant(X)\n",
    "    model = sm.OLS(y,X,missing='drop')\n",
    "    res = model.fit()\n",
    "    dds.append(res.resid)\n",
    "    \n",
    "df_beta_resid2 = pd.concat(dds,axis=1)\n",
    "df_beta_resid2.columns = close_ret.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0b71da1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af1cfd02bd76441da4e0f1f155c17d1e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1933 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dds = []\n",
    "for code in tqdm(close_ret.columns):\n",
    "    y = close_ret[code]\n",
    "    X = index_ret\n",
    "    X = sm.add_constant(X)\n",
    "    model = RollingOLS(endog=y,\n",
    "                       exog=X,\n",
    "                       window=252*2,\n",
    "                       expanding=True,\n",
    "                       min_nobs=42)\n",
    "\n",
    "    res = model.fit()\n",
    "    resid = y - (res.params * X).sum(1)\n",
    "    dds.append(resid)\n",
    "    \n",
    "df_beta_resid = pd.concat(dds,axis=1)\n",
    "df_beta_resid.columns = close_ret.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "788dc8e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "MV = np.exp(SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5d7d2bba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x21505fb5e50>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df_beta_resid.loc[df_beta_resid.index[0]]\n",
    "X = MV.loc[MV.index[0]]\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "model = sm.OLS(y,X,missing='drop')\n",
    "res = model.fit()\n",
    "\n",
    "plt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c4cab8b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "000001.SZ    3.861159e-19\n",
       "000002.SZ   -7.374175e-19\n",
       "000004.SZ   -1.061805e-19\n",
       "000005.SZ    5.551115e-19\n",
       "000006.SZ   -8.923605e-20\n",
       "                 ...     \n",
       "603995.SH   -5.384951e-19\n",
       "603996.SH   -1.186146e-18\n",
       "603997.SH    2.947896e-20\n",
       "603998.SH    7.106422e-19\n",
       "603999.SH    3.125410e-19\n",
       "Length: 1933, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_beta_resid2.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e27f723",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5a966ef",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a77325c2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e765f211",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dbe8f2a3610948de9870df5956e6f176",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1933 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dds = []\n",
    "for code in tqdm(BP.columns):\n",
    "    y = BP[code]\n",
    "    X = SIZE[code]\n",
    "    X = sm.add_constant(X)\n",
    "    model = sm.OLS(y,X,missing='drop')\n",
    "    res = model.fit()\n",
    "    dd = res.resid\n",
    "    dds.append(dd)\n",
    "    \n",
    "BP_resid = pd.concat(dds,axis=1).sort_index()\n",
    "BP_resid.columns = BP.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "88aff358",
   "metadata": {},
   "outputs": [],
   "source": [
    "BP_rank = BP.rank(axis=1)\n",
    "BP_resid_rank = BP_resid.rank(axis=1)\n",
    "SIZE_rank = SIZE.rank(axis=1)\n",
    "close_ret_rank = close_ret.rank(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "00d704cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f3b2e91605164bdfb64a277d6cd11a88",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2877 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp1 = []\n",
    "pp2 = []\n",
    "pp3 = []\n",
    "for i in tqdm(range(len(BP))):\n",
    "    df_corr = pd.concat([BP_rank.iloc[i],BP_resid_rank.iloc[i],SIZE_rank.iloc[i],close_ret_rank.iloc[i]],axis=1)\n",
    "    df_corr.columns = ['BP_resid','BP','SIZE','ret']\n",
    "    df_corr = df_corr.corr('spearman')\n",
    "    pp1.append(df_corr.loc['BP','ret'])\n",
    "    pp2.append(df_corr.loc['BP_resid','ret'])\n",
    "    pp3.append(df_corr.loc['SIZE','ret'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "25885092",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BP_resid</th>\n",
       "      <th>BP</th>\n",
       "      <th>SIZE</th>\n",
       "      <th>ret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000001.SZ</th>\n",
       "      <td>1415.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1908.0</td>\n",
       "      <td>511.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000002.SZ</th>\n",
       "      <td>1512.0</td>\n",
       "      <td>1659.0</td>\n",
       "      <td>1897.0</td>\n",
       "      <td>238.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000004.SZ</th>\n",
       "      <td>746.0</td>\n",
       "      <td>1448.0</td>\n",
       "      <td>543.0</td>\n",
       "      <td>1577.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000005.SZ</th>\n",
       "      <td>1169.0</td>\n",
       "      <td>1315.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>1847.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000006.SZ</th>\n",
       "      <td>1722.0</td>\n",
       "      <td>1792.0</td>\n",
       "      <td>919.0</td>\n",
       "      <td>719.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603995.SH</th>\n",
       "      <td>531.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>1181.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603996.SH</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>158.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603997.SH</th>\n",
       "      <td>1024.0</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>1110.0</td>\n",
       "      <td>1836.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603998.SH</th>\n",
       "      <td>891.0</td>\n",
       "      <td>664.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>1305.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603999.SH</th>\n",
       "      <td>1160.0</td>\n",
       "      <td>661.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>669.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1933 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           BP_resid      BP    SIZE     ret\n",
       "000001.SZ    1415.0    30.0  1908.0   511.0\n",
       "000002.SZ    1512.0  1659.0  1897.0   238.0\n",
       "000004.SZ     746.0  1448.0   543.0  1577.0\n",
       "000005.SZ    1169.0  1315.0   196.0  1847.0\n",
       "000006.SZ    1722.0  1792.0   919.0   719.0\n",
       "...             ...     ...     ...     ...\n",
       "603995.SH     531.0   354.0  1181.0   120.0\n",
       "603996.SH       NaN     NaN     2.0   158.0\n",
       "603997.SH    1024.0  1530.0  1110.0  1836.0\n",
       "603998.SH     891.0   664.0   291.0  1305.0\n",
       "603999.SH    1160.0   661.0   404.0   669.0\n",
       "\n",
       "[1933 rows x 4 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = 2800\n",
    "df_corr = pd.concat([BP_rank.iloc[i],BP_resid_rank.iloc[i],SIZE_rank.iloc[i],close_ret_rank.iloc[i]],axis=1)\n",
    "df_corr.columns = ['BP_resid','BP','SIZE','ret']\n",
    "df_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "95ea9c37",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>000001.SZ</th>\n",
       "      <th>000002.SZ</th>\n",
       "      <th>000004.SZ</th>\n",
       "      <th>000005.SZ</th>\n",
       "      <th>000006.SZ</th>\n",
       "      <th>000007.SZ</th>\n",
       "      <th>000008.SZ</th>\n",
       "      <th>000009.SZ</th>\n",
       "      <th>000010.SZ</th>\n",
       "      <th>000011.SZ</th>\n",
       "      <th>...</th>\n",
       "      <th>603989.SH</th>\n",
       "      <th>603990.SH</th>\n",
       "      <th>603991.SH</th>\n",
       "      <th>603992.SH</th>\n",
       "      <th>603993.SH</th>\n",
       "      <th>603995.SH</th>\n",
       "      <th>603996.SH</th>\n",
       "      <th>603997.SH</th>\n",
       "      <th>603998.SH</th>\n",
       "      <th>603999.SH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>0.259249</td>\n",
       "      <td>0.299814</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.135157</td>\n",
       "      <td>0.334258</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.160154</td>\n",
       "      <td>0.026386</td>\n",
       "      <td>0.114291</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>0.263810</td>\n",
       "      <td>0.306758</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.130579</td>\n",
       "      <td>0.344163</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.164401</td>\n",
       "      <td>0.026864</td>\n",
       "      <td>0.117950</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>0.268420</td>\n",
       "      <td>0.306758</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.133156</td>\n",
       "      <td>0.343206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.164558</td>\n",
       "      <td>0.026589</td>\n",
       "      <td>0.117607</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>0.271378</td>\n",
       "      <td>0.309148</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.138866</td>\n",
       "      <td>0.346081</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.161496</td>\n",
       "      <td>0.027675</td>\n",
       "      <td>0.120402</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>0.271983</td>\n",
       "      <td>0.307050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.137451</td>\n",
       "      <td>0.337895</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.154043</td>\n",
       "      <td>0.027192</td>\n",
       "      <td>0.119222</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-01</th>\n",
       "      <td>0.847529</td>\n",
       "      <td>1.076426</td>\n",
       "      <td>0.567376</td>\n",
       "      <td>0.627628</td>\n",
       "      <td>1.365001</td>\n",
       "      <td>0.039004</td>\n",
       "      <td>1.068719</td>\n",
       "      <td>0.146724</td>\n",
       "      <td>0.156255</td>\n",
       "      <td>0.670826</td>\n",
       "      <td>...</td>\n",
       "      <td>0.193885</td>\n",
       "      <td>0.318522</td>\n",
       "      <td>0.108844</td>\n",
       "      <td>0.282374</td>\n",
       "      <td>0.304535</td>\n",
       "      <td>0.342689</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.398073</td>\n",
       "      <td>0.527009</td>\n",
       "      <td>0.641807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-02</th>\n",
       "      <td>0.903914</td>\n",
       "      <td>1.110001</td>\n",
       "      <td>0.582954</td>\n",
       "      <td>0.654922</td>\n",
       "      <td>1.391788</td>\n",
       "      <td>0.038585</td>\n",
       "      <td>1.083658</td>\n",
       "      <td>0.152760</td>\n",
       "      <td>0.161064</td>\n",
       "      <td>0.683667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.195271</td>\n",
       "      <td>0.327955</td>\n",
       "      <td>0.111417</td>\n",
       "      <td>0.283728</td>\n",
       "      <td>0.312881</td>\n",
       "      <td>0.342618</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.404907</td>\n",
       "      <td>0.532085</td>\n",
       "      <td>0.655308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-03</th>\n",
       "      <td>0.911494</td>\n",
       "      <td>1.084716</td>\n",
       "      <td>0.571102</td>\n",
       "      <td>0.648635</td>\n",
       "      <td>1.378360</td>\n",
       "      <td>0.036766</td>\n",
       "      <td>1.088732</td>\n",
       "      <td>0.157808</td>\n",
       "      <td>0.159834</td>\n",
       "      <td>0.679117</td>\n",
       "      <td>...</td>\n",
       "      <td>0.190505</td>\n",
       "      <td>0.327279</td>\n",
       "      <td>0.111079</td>\n",
       "      <td>0.278823</td>\n",
       "      <td>0.309693</td>\n",
       "      <td>0.340483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.404531</td>\n",
       "      <td>0.529017</td>\n",
       "      <td>0.648508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-04</th>\n",
       "      <td>0.919625</td>\n",
       "      <td>1.084128</td>\n",
       "      <td>0.568731</td>\n",
       "      <td>0.648635</td>\n",
       "      <td>1.378360</td>\n",
       "      <td>0.035112</td>\n",
       "      <td>1.099022</td>\n",
       "      <td>0.152760</td>\n",
       "      <td>0.160653</td>\n",
       "      <td>0.677186</td>\n",
       "      <td>...</td>\n",
       "      <td>0.173193</td>\n",
       "      <td>0.323164</td>\n",
       "      <td>0.109620</td>\n",
       "      <td>0.279705</td>\n",
       "      <td>0.322258</td>\n",
       "      <td>0.332723</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.394384</td>\n",
       "      <td>0.528011</td>\n",
       "      <td>0.645828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-05</th>\n",
       "      <td>0.931619</td>\n",
       "      <td>1.098780</td>\n",
       "      <td>0.556143</td>\n",
       "      <td>0.651763</td>\n",
       "      <td>1.388310</td>\n",
       "      <td>0.036101</td>\n",
       "      <td>1.099022</td>\n",
       "      <td>0.163559</td>\n",
       "      <td>0.163156</td>\n",
       "      <td>0.688279</td>\n",
       "      <td>...</td>\n",
       "      <td>0.173361</td>\n",
       "      <td>0.326286</td>\n",
       "      <td>0.109143</td>\n",
       "      <td>0.282829</td>\n",
       "      <td>0.332823</td>\n",
       "      <td>0.317723</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.384394</td>\n",
       "      <td>0.529017</td>\n",
       "      <td>0.636578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2877 rows × 1933 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            000001.SZ  000002.SZ  000004.SZ  000005.SZ  000006.SZ  000007.SZ  \\\n",
       "trade_date                                                                     \n",
       "2010-01-04   0.259249   0.299814        NaN   0.135157   0.334258        NaN   \n",
       "2010-01-05   0.263810   0.306758        NaN   0.130579   0.344163        NaN   \n",
       "2010-01-06   0.268420   0.306758        NaN   0.133156   0.343206        NaN   \n",
       "2010-01-07   0.271378   0.309148        NaN   0.138866   0.346081        NaN   \n",
       "2010-01-08   0.271983   0.307050        NaN   0.137451   0.337895        NaN   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "2021-11-01   0.847529   1.076426   0.567376   0.627628   1.365001   0.039004   \n",
       "2021-11-02   0.903914   1.110001   0.582954   0.654922   1.391788   0.038585   \n",
       "2021-11-03   0.911494   1.084716   0.571102   0.648635   1.378360   0.036766   \n",
       "2021-11-04   0.919625   1.084128   0.568731   0.648635   1.378360   0.035112   \n",
       "2021-11-05   0.931619   1.098780   0.556143   0.651763   1.388310   0.036101   \n",
       "\n",
       "            000008.SZ  000009.SZ  000010.SZ  000011.SZ  ...  603989.SH  \\\n",
       "trade_date                                              ...              \n",
       "2010-01-04        NaN   0.160154   0.026386   0.114291  ...        NaN   \n",
       "2010-01-05        NaN   0.164401   0.026864   0.117950  ...        NaN   \n",
       "2010-01-06        NaN   0.164558   0.026589   0.117607  ...        NaN   \n",
       "2010-01-07        NaN   0.161496   0.027675   0.120402  ...        NaN   \n",
       "2010-01-08        NaN   0.154043   0.027192   0.119222  ...        NaN   \n",
       "...               ...        ...        ...        ...  ...        ...   \n",
       "2021-11-01   1.068719   0.146724   0.156255   0.670826  ...   0.193885   \n",
       "2021-11-02   1.083658   0.152760   0.161064   0.683667  ...   0.195271   \n",
       "2021-11-03   1.088732   0.157808   0.159834   0.679117  ...   0.190505   \n",
       "2021-11-04   1.099022   0.152760   0.160653   0.677186  ...   0.173193   \n",
       "2021-11-05   1.099022   0.163559   0.163156   0.688279  ...   0.173361   \n",
       "\n",
       "            603990.SH  603991.SH  603992.SH  603993.SH  603995.SH  603996.SH  \\\n",
       "trade_date                                                                     \n",
       "2010-01-04        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-05        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-06        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-07        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-08        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "2021-11-01   0.318522   0.108844   0.282374   0.304535   0.342689        NaN   \n",
       "2021-11-02   0.327955   0.111417   0.283728   0.312881   0.342618        NaN   \n",
       "2021-11-03   0.327279   0.111079   0.278823   0.309693   0.340483        NaN   \n",
       "2021-11-04   0.323164   0.109620   0.279705   0.322258   0.332723        NaN   \n",
       "2021-11-05   0.326286   0.109143   0.282829   0.332823   0.317723        NaN   \n",
       "\n",
       "            603997.SH  603998.SH  603999.SH  \n",
       "trade_date                                   \n",
       "2010-01-04        NaN        NaN        NaN  \n",
       "2010-01-05        NaN        NaN        NaN  \n",
       "2010-01-06        NaN        NaN        NaN  \n",
       "2010-01-07        NaN        NaN        NaN  \n",
       "2010-01-08        NaN        NaN        NaN  \n",
       "...               ...        ...        ...  \n",
       "2021-11-01   0.398073   0.527009   0.641807  \n",
       "2021-11-02   0.404907   0.532085   0.655308  \n",
       "2021-11-03   0.404531   0.529017   0.648508  \n",
       "2021-11-04   0.394384   0.528011   0.645828  \n",
       "2021-11-05   0.384394   0.529017   0.636578  \n",
       "\n",
       "[2877 rows x 1933 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4e8b51c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "8ef6f836",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1bf086bcdc0>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(pp1,index=BP.index).cumsum().plot(figsize=(20,6),grid=True,label='BP')\n",
    "pd.Series(pp2,index=BP.index).cumsum().plot(figsize=(20,6),grid=True,label='BP_resid')\n",
    "pd.Series(pp3,index=BP.index).cumsum().plot(figsize=(20,6),grid=True,label='SIZE')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "ebc992eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1bf19f9e8b0>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(pp1,index=BP.index).cumsum().plot(figsize=(20,6),grid=True,label='BP')\n",
    "pd.Series(pp2,index=BP.index).cumsum().plot(figsize=(20,6),grid=True,label='BP_resid')\n",
    "pd.Series(pp3,index=BP.index).cumsum().plot(figsize=(20,6),grid=True,label='SIZE')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "5cb2b9ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " nan]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp3[-1000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "7ffa76bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   5.,   25.,  130.,  565., 1030.,  780.,  255.,   68.,   11.,\n",
       "           7.]),\n",
       " array([-0.26129778, -0.20340249, -0.1455072 , -0.08761191, -0.02971661,\n",
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       "         0.31765513]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zKEN/QFW17C8kydNJtlbVue7SwPll+r2AhcD/cFXdu0ql9m0l5rTG9fP4j0vpESGXUq396mtOSV4N3A28qaq+MabahjXQ76mqPp3kp5JsqapVe8Ccl3dW1jFgb7e+F7hvcYckAT4AnKyq946xtmFddE6XgH4e/3EMeGv3LZ4bgO/86LLWGrQeH2dy0Tkl+UngXuAtVfXVCdQ4qH7m9NNdJtB9Y+wyYHXfzCb9Cfd6+gF+AjgOnO6Wm7v2lwP/0K2/gYW/4n0JeKT7uWXStY8yp277I8A5Fj44nAP2Tbr2RfO4hYVvSn0NeFfX9jbgbd16WPgHfr4GfBmYmXTNI87nZd3v4bvAt7v1Kydd94hzuhv4Vs//N7OTrnkF5vQnwIluPp8F3rDaNfkYBklqiJd3JKkhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkMMfUlqyP8CzX1shzV2JIUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(pp1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "e99bada3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='trade_date'>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(pp,index=BP.index).cumsum().plot(figsize=(20,6),grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "077f493b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8638f1e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0c53c78",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09f14227",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9978caa6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f0889ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0362a78c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "7cbff40f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BP_resid</th>\n",
       "      <th>BP</th>\n",
       "      <th>SIZE</th>\n",
       "      <th>ret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BP_resid</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.583953</td>\n",
       "      <td>0.453503</td>\n",
       "      <td>0.013749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BP</th>\n",
       "      <td>-0.583953</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.899133</td>\n",
       "      <td>0.032234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SIZE</th>\n",
       "      <td>0.453503</td>\n",
       "      <td>-0.899133</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.115617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ret</th>\n",
       "      <td>0.013749</td>\n",
       "      <td>0.032234</td>\n",
       "      <td>-0.115617</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          BP_resid        BP      SIZE       ret\n",
       "BP_resid  1.000000 -0.583953  0.453503  0.013749\n",
       "BP       -0.583953  1.000000 -0.899133  0.032234\n",
       "SIZE      0.453503 -0.899133  1.000000 -0.115617\n",
       "ret       0.013749  0.032234 -0.115617  1.000000"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BP_rank = BP.rank(axis=1)\n",
    "BP_resid_rank = BP_resid.rank(axis=1)\n",
    "SIZE_rank = SIZE.rank(axis=1)\n",
    "close_ret_rank = close_ret.rank(axis=1)\n",
    "df_corr = pd.concat([BP_resid_rank,BP_rank,SIZE_rank,close_ret_rank],axis=1)\n",
    "df_corr.columns = ['BP_resid','BP','SIZE','ret']\n",
    "df_corr.corr('spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "8d2f38ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>BP_resid</th>\n",
       "      <th>BP</th>\n",
       "      <th>SIZE</th>\n",
       "      <th>ret</th>\n",
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       "    <tr>\n",
       "      <th>BP_resid</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.125315</td>\n",
       "      <td>0.037821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BP</th>\n",
       "      <td>-0.248148</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.919533</td>\n",
       "      <td>0.035258</td>\n",
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       "    <tr>\n",
       "      <th>SIZE</th>\n",
       "      <td>0.125315</td>\n",
       "      <td>-0.919533</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.069268</td>\n",
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       "    <tr>\n",
       "      <th>ret</th>\n",
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      ],
      "text/plain": [
       "          BP_resid        BP      SIZE       ret\n",
       "BP_resid  1.000000 -0.248148  0.125315  0.037821\n",
       "BP       -0.248148  1.000000 -0.919533  0.035258\n",
       "SIZE      0.125315 -0.919533  1.000000 -0.069268\n",
       "ret       0.037821  0.035258 -0.069268  1.000000"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BP_rank = BP.rank(axis=1)\n",
    "BP_resid_rank = BP_resid.rank(axis=1)\n",
    "SIZE_rank = SIZE.rank(axis=1)\n",
    "df_corr = pd.concat([BP_resid_rank[code],BP_rank[code],SIZE_rank[code],close_ret[code]],axis=1)\n",
    "df_corr.columns = ['BP_resid','BP','SIZE','ret']\n",
    "df_corr.corr('spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "61dab82c",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>000001.SZ</th>\n",
       "      <th>000002.SZ</th>\n",
       "      <th>000004.SZ</th>\n",
       "      <th>000005.SZ</th>\n",
       "      <th>000006.SZ</th>\n",
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       "      <th>000008.SZ</th>\n",
       "      <th>000009.SZ</th>\n",
       "      <th>000010.SZ</th>\n",
       "      <th>000011.SZ</th>\n",
       "      <th>...</th>\n",
       "      <th>603989.SH</th>\n",
       "      <th>603990.SH</th>\n",
       "      <th>603991.SH</th>\n",
       "      <th>603992.SH</th>\n",
       "      <th>603993.SH</th>\n",
       "      <th>603995.SH</th>\n",
       "      <th>603996.SH</th>\n",
       "      <th>603997.SH</th>\n",
       "      <th>603998.SH</th>\n",
       "      <th>603999.SH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>0.259249</td>\n",
       "      <td>0.299814</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.135157</td>\n",
       "      <td>0.334258</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.160154</td>\n",
       "      <td>0.026386</td>\n",
       "      <td>0.114291</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>0.263810</td>\n",
       "      <td>0.306758</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.130579</td>\n",
       "      <td>0.344163</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.164401</td>\n",
       "      <td>0.026864</td>\n",
       "      <td>0.117950</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>0.268420</td>\n",
       "      <td>0.306758</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.133156</td>\n",
       "      <td>0.343206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.164558</td>\n",
       "      <td>0.026589</td>\n",
       "      <td>0.117607</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>0.271378</td>\n",
       "      <td>0.309148</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.138866</td>\n",
       "      <td>0.346081</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.161496</td>\n",
       "      <td>0.027675</td>\n",
       "      <td>0.120402</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>0.271983</td>\n",
       "      <td>0.307050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.137451</td>\n",
       "      <td>0.337895</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.154043</td>\n",
       "      <td>0.027192</td>\n",
       "      <td>0.119222</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-01</th>\n",
       "      <td>0.847529</td>\n",
       "      <td>1.076426</td>\n",
       "      <td>0.567376</td>\n",
       "      <td>0.627628</td>\n",
       "      <td>1.365001</td>\n",
       "      <td>0.039004</td>\n",
       "      <td>1.068719</td>\n",
       "      <td>0.146724</td>\n",
       "      <td>0.156255</td>\n",
       "      <td>0.670826</td>\n",
       "      <td>...</td>\n",
       "      <td>0.193885</td>\n",
       "      <td>0.318522</td>\n",
       "      <td>0.108844</td>\n",
       "      <td>0.282374</td>\n",
       "      <td>0.304535</td>\n",
       "      <td>0.342689</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.398073</td>\n",
       "      <td>0.527009</td>\n",
       "      <td>0.641807</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-02</th>\n",
       "      <td>0.903914</td>\n",
       "      <td>1.110001</td>\n",
       "      <td>0.582954</td>\n",
       "      <td>0.654922</td>\n",
       "      <td>1.391788</td>\n",
       "      <td>0.038585</td>\n",
       "      <td>1.083658</td>\n",
       "      <td>0.152760</td>\n",
       "      <td>0.161064</td>\n",
       "      <td>0.683667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.195271</td>\n",
       "      <td>0.327955</td>\n",
       "      <td>0.111417</td>\n",
       "      <td>0.283728</td>\n",
       "      <td>0.312881</td>\n",
       "      <td>0.342618</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.404907</td>\n",
       "      <td>0.532085</td>\n",
       "      <td>0.655308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-03</th>\n",
       "      <td>0.911494</td>\n",
       "      <td>1.084716</td>\n",
       "      <td>0.571102</td>\n",
       "      <td>0.648635</td>\n",
       "      <td>1.378360</td>\n",
       "      <td>0.036766</td>\n",
       "      <td>1.088732</td>\n",
       "      <td>0.157808</td>\n",
       "      <td>0.159834</td>\n",
       "      <td>0.679117</td>\n",
       "      <td>...</td>\n",
       "      <td>0.190505</td>\n",
       "      <td>0.327279</td>\n",
       "      <td>0.111079</td>\n",
       "      <td>0.278823</td>\n",
       "      <td>0.309693</td>\n",
       "      <td>0.340483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.404531</td>\n",
       "      <td>0.529017</td>\n",
       "      <td>0.648508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-04</th>\n",
       "      <td>0.919625</td>\n",
       "      <td>1.084128</td>\n",
       "      <td>0.568731</td>\n",
       "      <td>0.648635</td>\n",
       "      <td>1.378360</td>\n",
       "      <td>0.035112</td>\n",
       "      <td>1.099022</td>\n",
       "      <td>0.152760</td>\n",
       "      <td>0.160653</td>\n",
       "      <td>0.677186</td>\n",
       "      <td>...</td>\n",
       "      <td>0.173193</td>\n",
       "      <td>0.323164</td>\n",
       "      <td>0.109620</td>\n",
       "      <td>0.279705</td>\n",
       "      <td>0.322258</td>\n",
       "      <td>0.332723</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.394384</td>\n",
       "      <td>0.528011</td>\n",
       "      <td>0.645828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-05</th>\n",
       "      <td>0.931619</td>\n",
       "      <td>1.098780</td>\n",
       "      <td>0.556143</td>\n",
       "      <td>0.651763</td>\n",
       "      <td>1.388310</td>\n",
       "      <td>0.036101</td>\n",
       "      <td>1.099022</td>\n",
       "      <td>0.163559</td>\n",
       "      <td>0.163156</td>\n",
       "      <td>0.688279</td>\n",
       "      <td>...</td>\n",
       "      <td>0.173361</td>\n",
       "      <td>0.326286</td>\n",
       "      <td>0.109143</td>\n",
       "      <td>0.282829</td>\n",
       "      <td>0.332823</td>\n",
       "      <td>0.317723</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.384394</td>\n",
       "      <td>0.529017</td>\n",
       "      <td>0.636578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2877 rows × 1933 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            000001.SZ  000002.SZ  000004.SZ  000005.SZ  000006.SZ  000007.SZ  \\\n",
       "trade_date                                                                     \n",
       "2010-01-04   0.259249   0.299814        NaN   0.135157   0.334258        NaN   \n",
       "2010-01-05   0.263810   0.306758        NaN   0.130579   0.344163        NaN   \n",
       "2010-01-06   0.268420   0.306758        NaN   0.133156   0.343206        NaN   \n",
       "2010-01-07   0.271378   0.309148        NaN   0.138866   0.346081        NaN   \n",
       "2010-01-08   0.271983   0.307050        NaN   0.137451   0.337895        NaN   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "2021-11-01   0.847529   1.076426   0.567376   0.627628   1.365001   0.039004   \n",
       "2021-11-02   0.903914   1.110001   0.582954   0.654922   1.391788   0.038585   \n",
       "2021-11-03   0.911494   1.084716   0.571102   0.648635   1.378360   0.036766   \n",
       "2021-11-04   0.919625   1.084128   0.568731   0.648635   1.378360   0.035112   \n",
       "2021-11-05   0.931619   1.098780   0.556143   0.651763   1.388310   0.036101   \n",
       "\n",
       "            000008.SZ  000009.SZ  000010.SZ  000011.SZ  ...  603989.SH  \\\n",
       "trade_date                                              ...              \n",
       "2010-01-04        NaN   0.160154   0.026386   0.114291  ...        NaN   \n",
       "2010-01-05        NaN   0.164401   0.026864   0.117950  ...        NaN   \n",
       "2010-01-06        NaN   0.164558   0.026589   0.117607  ...        NaN   \n",
       "2010-01-07        NaN   0.161496   0.027675   0.120402  ...        NaN   \n",
       "2010-01-08        NaN   0.154043   0.027192   0.119222  ...        NaN   \n",
       "...               ...        ...        ...        ...  ...        ...   \n",
       "2021-11-01   1.068719   0.146724   0.156255   0.670826  ...   0.193885   \n",
       "2021-11-02   1.083658   0.152760   0.161064   0.683667  ...   0.195271   \n",
       "2021-11-03   1.088732   0.157808   0.159834   0.679117  ...   0.190505   \n",
       "2021-11-04   1.099022   0.152760   0.160653   0.677186  ...   0.173193   \n",
       "2021-11-05   1.099022   0.163559   0.163156   0.688279  ...   0.173361   \n",
       "\n",
       "            603990.SH  603991.SH  603992.SH  603993.SH  603995.SH  603996.SH  \\\n",
       "trade_date                                                                     \n",
       "2010-01-04        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-05        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-06        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-07        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-08        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "2021-11-01   0.318522   0.108844   0.282374   0.304535   0.342689        NaN   \n",
       "2021-11-02   0.327955   0.111417   0.283728   0.312881   0.342618        NaN   \n",
       "2021-11-03   0.327279   0.111079   0.278823   0.309693   0.340483        NaN   \n",
       "2021-11-04   0.323164   0.109620   0.279705   0.322258   0.332723        NaN   \n",
       "2021-11-05   0.326286   0.109143   0.282829   0.332823   0.317723        NaN   \n",
       "\n",
       "            603997.SH  603998.SH  603999.SH  \n",
       "trade_date                                   \n",
       "2010-01-04        NaN        NaN        NaN  \n",
       "2010-01-05        NaN        NaN        NaN  \n",
       "2010-01-06        NaN        NaN        NaN  \n",
       "2010-01-07        NaN        NaN        NaN  \n",
       "2010-01-08        NaN        NaN        NaN  \n",
       "...               ...        ...        ...  \n",
       "2021-11-01   0.398073   0.527009   0.641807  \n",
       "2021-11-02   0.404907   0.532085   0.655308  \n",
       "2021-11-03   0.404531   0.529017   0.648508  \n",
       "2021-11-04   0.394384   0.528011   0.645828  \n",
       "2021-11-05   0.384394   0.529017   0.636578  \n",
       "\n",
       "[2877 rows x 1933 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ddd9199",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e8aec73d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "code = '601012.SH'\n",
    "\n",
    "y = close_ret[code].shift(-1)\n",
    "X = pd.concat([index_ret,SIZE[code],BP[code]],axis=1)\n",
    "X.columns = ['index_ret','SIZE','BP']\n",
    "X = sm.add_constant(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "646fc2b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>000001.SZ</th>\n",
       "      <th>000002.SZ</th>\n",
       "      <th>000004.SZ</th>\n",
       "      <th>000005.SZ</th>\n",
       "      <th>000006.SZ</th>\n",
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       "      <th>603993.SH</th>\n",
       "      <th>603995.SH</th>\n",
       "      <th>603996.SH</th>\n",
       "      <th>603997.SH</th>\n",
       "      <th>603998.SH</th>\n",
       "      <th>603999.SH</th>\n",
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       "    <tr>\n",
       "      <th>trade_date</th>\n",
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       "      <th>2010-01-04</th>\n",
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       "      <td>0.135157</td>\n",
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       "      <td>0.160154</td>\n",
       "      <td>0.026386</td>\n",
       "      <td>0.114291</td>\n",
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       "      <th>2010-01-05</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>0.268420</td>\n",
       "      <td>0.306758</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.133156</td>\n",
       "      <td>0.343206</td>\n",
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       "      <td>0.117607</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2010-01-07</th>\n",
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       "      <td>0.138866</td>\n",
       "      <td>0.346081</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.161496</td>\n",
       "      <td>0.027675</td>\n",
       "      <td>0.120402</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2010-01-08</th>\n",
       "      <td>0.271983</td>\n",
       "      <td>0.307050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.137451</td>\n",
       "      <td>0.337895</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2021-11-01</th>\n",
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       "      <td>1.076426</td>\n",
       "      <td>0.567376</td>\n",
       "      <td>0.627628</td>\n",
       "      <td>1.365001</td>\n",
       "      <td>0.039004</td>\n",
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       "      <td>0.146724</td>\n",
       "      <td>0.156255</td>\n",
       "      <td>0.670826</td>\n",
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       "      <td>0.193885</td>\n",
       "      <td>0.318522</td>\n",
       "      <td>0.108844</td>\n",
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       "      <td>0.304535</td>\n",
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       "      <td>0.398073</td>\n",
       "      <td>0.527009</td>\n",
       "      <td>0.641807</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-02</th>\n",
       "      <td>0.903914</td>\n",
       "      <td>1.110001</td>\n",
       "      <td>0.582954</td>\n",
       "      <td>0.654922</td>\n",
       "      <td>1.391788</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-03</th>\n",
       "      <td>0.911494</td>\n",
       "      <td>1.084716</td>\n",
       "      <td>0.571102</td>\n",
       "      <td>0.648635</td>\n",
       "      <td>1.378360</td>\n",
       "      <td>0.036766</td>\n",
       "      <td>1.088732</td>\n",
       "      <td>0.157808</td>\n",
       "      <td>0.159834</td>\n",
       "      <td>0.679117</td>\n",
       "      <td>...</td>\n",
       "      <td>0.190505</td>\n",
       "      <td>0.327279</td>\n",
       "      <td>0.111079</td>\n",
       "      <td>0.278823</td>\n",
       "      <td>0.309693</td>\n",
       "      <td>0.340483</td>\n",
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       "      <td>0.404531</td>\n",
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       "      <td>0.648508</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-04</th>\n",
       "      <td>0.919625</td>\n",
       "      <td>1.084128</td>\n",
       "      <td>0.568731</td>\n",
       "      <td>0.648635</td>\n",
       "      <td>1.378360</td>\n",
       "      <td>0.035112</td>\n",
       "      <td>1.099022</td>\n",
       "      <td>0.152760</td>\n",
       "      <td>0.160653</td>\n",
       "      <td>0.677186</td>\n",
       "      <td>...</td>\n",
       "      <td>0.173193</td>\n",
       "      <td>0.323164</td>\n",
       "      <td>0.109620</td>\n",
       "      <td>0.279705</td>\n",
       "      <td>0.322258</td>\n",
       "      <td>0.332723</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.394384</td>\n",
       "      <td>0.528011</td>\n",
       "      <td>0.645828</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-05</th>\n",
       "      <td>0.931619</td>\n",
       "      <td>1.098780</td>\n",
       "      <td>0.556143</td>\n",
       "      <td>0.651763</td>\n",
       "      <td>1.388310</td>\n",
       "      <td>0.036101</td>\n",
       "      <td>1.099022</td>\n",
       "      <td>0.163559</td>\n",
       "      <td>0.163156</td>\n",
       "      <td>0.688279</td>\n",
       "      <td>...</td>\n",
       "      <td>0.173361</td>\n",
       "      <td>0.326286</td>\n",
       "      <td>0.109143</td>\n",
       "      <td>0.282829</td>\n",
       "      <td>0.332823</td>\n",
       "      <td>0.317723</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.384394</td>\n",
       "      <td>0.529017</td>\n",
       "      <td>0.636578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2877 rows × 1933 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            000001.SZ  000002.SZ  000004.SZ  000005.SZ  000006.SZ  000007.SZ  \\\n",
       "trade_date                                                                     \n",
       "2010-01-04   0.259249   0.299814        NaN   0.135157   0.334258        NaN   \n",
       "2010-01-05   0.263810   0.306758        NaN   0.130579   0.344163        NaN   \n",
       "2010-01-06   0.268420   0.306758        NaN   0.133156   0.343206        NaN   \n",
       "2010-01-07   0.271378   0.309148        NaN   0.138866   0.346081        NaN   \n",
       "2010-01-08   0.271983   0.307050        NaN   0.137451   0.337895        NaN   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "2021-11-01   0.847529   1.076426   0.567376   0.627628   1.365001   0.039004   \n",
       "2021-11-02   0.903914   1.110001   0.582954   0.654922   1.391788   0.038585   \n",
       "2021-11-03   0.911494   1.084716   0.571102   0.648635   1.378360   0.036766   \n",
       "2021-11-04   0.919625   1.084128   0.568731   0.648635   1.378360   0.035112   \n",
       "2021-11-05   0.931619   1.098780   0.556143   0.651763   1.388310   0.036101   \n",
       "\n",
       "            000008.SZ  000009.SZ  000010.SZ  000011.SZ  ...  603989.SH  \\\n",
       "trade_date                                              ...              \n",
       "2010-01-04        NaN   0.160154   0.026386   0.114291  ...        NaN   \n",
       "2010-01-05        NaN   0.164401   0.026864   0.117950  ...        NaN   \n",
       "2010-01-06        NaN   0.164558   0.026589   0.117607  ...        NaN   \n",
       "2010-01-07        NaN   0.161496   0.027675   0.120402  ...        NaN   \n",
       "2010-01-08        NaN   0.154043   0.027192   0.119222  ...        NaN   \n",
       "...               ...        ...        ...        ...  ...        ...   \n",
       "2021-11-01   1.068719   0.146724   0.156255   0.670826  ...   0.193885   \n",
       "2021-11-02   1.083658   0.152760   0.161064   0.683667  ...   0.195271   \n",
       "2021-11-03   1.088732   0.157808   0.159834   0.679117  ...   0.190505   \n",
       "2021-11-04   1.099022   0.152760   0.160653   0.677186  ...   0.173193   \n",
       "2021-11-05   1.099022   0.163559   0.163156   0.688279  ...   0.173361   \n",
       "\n",
       "            603990.SH  603991.SH  603992.SH  603993.SH  603995.SH  603996.SH  \\\n",
       "trade_date                                                                     \n",
       "2010-01-04        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-05        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-06        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-07        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-08        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "2021-11-01   0.318522   0.108844   0.282374   0.304535   0.342689        NaN   \n",
       "2021-11-02   0.327955   0.111417   0.283728   0.312881   0.342618        NaN   \n",
       "2021-11-03   0.327279   0.111079   0.278823   0.309693   0.340483        NaN   \n",
       "2021-11-04   0.323164   0.109620   0.279705   0.322258   0.332723        NaN   \n",
       "2021-11-05   0.326286   0.109143   0.282829   0.332823   0.317723        NaN   \n",
       "\n",
       "            603997.SH  603998.SH  603999.SH  \n",
       "trade_date                                   \n",
       "2010-01-04        NaN        NaN        NaN  \n",
       "2010-01-05        NaN        NaN        NaN  \n",
       "2010-01-06        NaN        NaN        NaN  \n",
       "2010-01-07        NaN        NaN        NaN  \n",
       "2010-01-08        NaN        NaN        NaN  \n",
       "...               ...        ...        ...  \n",
       "2021-11-01   0.398073   0.527009   0.641807  \n",
       "2021-11-02   0.404907   0.532085   0.655308  \n",
       "2021-11-03   0.404531   0.529017   0.648508  \n",
       "2021-11-04   0.394384   0.528011   0.645828  \n",
       "2021-11-05   0.384394   0.529017   0.636578  \n",
       "\n",
       "[2877 rows x 1933 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1c40e1c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>0.259249</td>\n",
       "      <td>0.299814</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.135157</td>\n",
       "      <td>0.334258</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.160154</td>\n",
       "      <td>0.026386</td>\n",
       "      <td>0.114291</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>0.263810</td>\n",
       "      <td>0.306758</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.130579</td>\n",
       "      <td>0.344163</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.164401</td>\n",
       "      <td>0.026864</td>\n",
       "      <td>0.117950</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>0.268420</td>\n",
       "      <td>0.306758</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.133156</td>\n",
       "      <td>0.343206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.164558</td>\n",
       "      <td>0.026589</td>\n",
       "      <td>0.117607</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>0.271378</td>\n",
       "      <td>0.309148</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.138866</td>\n",
       "      <td>0.346081</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.161496</td>\n",
       "      <td>0.027675</td>\n",
       "      <td>0.120402</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>0.271983</td>\n",
       "      <td>0.307050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.137451</td>\n",
       "      <td>0.337895</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.154043</td>\n",
       "      <td>0.027192</td>\n",
       "      <td>0.119222</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-01</th>\n",
       "      <td>0.847529</td>\n",
       "      <td>1.076426</td>\n",
       "      <td>0.567376</td>\n",
       "      <td>0.627628</td>\n",
       "      <td>1.365001</td>\n",
       "      <td>0.039004</td>\n",
       "      <td>1.068719</td>\n",
       "      <td>0.146724</td>\n",
       "      <td>0.156255</td>\n",
       "      <td>0.670826</td>\n",
       "      <td>...</td>\n",
       "      <td>0.193885</td>\n",
       "      <td>0.318522</td>\n",
       "      <td>0.108844</td>\n",
       "      <td>0.282374</td>\n",
       "      <td>0.304535</td>\n",
       "      <td>0.342689</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.398073</td>\n",
       "      <td>0.527009</td>\n",
       "      <td>0.641807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-02</th>\n",
       "      <td>0.903914</td>\n",
       "      <td>1.110001</td>\n",
       "      <td>0.582954</td>\n",
       "      <td>0.654922</td>\n",
       "      <td>1.391788</td>\n",
       "      <td>0.038585</td>\n",
       "      <td>1.083658</td>\n",
       "      <td>0.152760</td>\n",
       "      <td>0.161064</td>\n",
       "      <td>0.683667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.195271</td>\n",
       "      <td>0.327955</td>\n",
       "      <td>0.111417</td>\n",
       "      <td>0.283728</td>\n",
       "      <td>0.312881</td>\n",
       "      <td>0.342618</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.404907</td>\n",
       "      <td>0.532085</td>\n",
       "      <td>0.655308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-03</th>\n",
       "      <td>0.911494</td>\n",
       "      <td>1.084716</td>\n",
       "      <td>0.571102</td>\n",
       "      <td>0.648635</td>\n",
       "      <td>1.378360</td>\n",
       "      <td>0.036766</td>\n",
       "      <td>1.088732</td>\n",
       "      <td>0.157808</td>\n",
       "      <td>0.159834</td>\n",
       "      <td>0.679117</td>\n",
       "      <td>...</td>\n",
       "      <td>0.190505</td>\n",
       "      <td>0.327279</td>\n",
       "      <td>0.111079</td>\n",
       "      <td>0.278823</td>\n",
       "      <td>0.309693</td>\n",
       "      <td>0.340483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.404531</td>\n",
       "      <td>0.529017</td>\n",
       "      <td>0.648508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-04</th>\n",
       "      <td>0.919625</td>\n",
       "      <td>1.084128</td>\n",
       "      <td>0.568731</td>\n",
       "      <td>0.648635</td>\n",
       "      <td>1.378360</td>\n",
       "      <td>0.035112</td>\n",
       "      <td>1.099022</td>\n",
       "      <td>0.152760</td>\n",
       "      <td>0.160653</td>\n",
       "      <td>0.677186</td>\n",
       "      <td>...</td>\n",
       "      <td>0.173193</td>\n",
       "      <td>0.323164</td>\n",
       "      <td>0.109620</td>\n",
       "      <td>0.279705</td>\n",
       "      <td>0.322258</td>\n",
       "      <td>0.332723</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.394384</td>\n",
       "      <td>0.528011</td>\n",
       "      <td>0.645828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-05</th>\n",
       "      <td>0.931619</td>\n",
       "      <td>1.098780</td>\n",
       "      <td>0.556143</td>\n",
       "      <td>0.651763</td>\n",
       "      <td>1.388310</td>\n",
       "      <td>0.036101</td>\n",
       "      <td>1.099022</td>\n",
       "      <td>0.163559</td>\n",
       "      <td>0.163156</td>\n",
       "      <td>0.688279</td>\n",
       "      <td>...</td>\n",
       "      <td>0.173361</td>\n",
       "      <td>0.326286</td>\n",
       "      <td>0.109143</td>\n",
       "      <td>0.282829</td>\n",
       "      <td>0.332823</td>\n",
       "      <td>0.317723</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.384394</td>\n",
       "      <td>0.529017</td>\n",
       "      <td>0.636578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2877 rows × 1933 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            000001.SZ  000002.SZ  000004.SZ  000005.SZ  000006.SZ  000007.SZ  \\\n",
       "trade_date                                                                     \n",
       "2010-01-04   0.259249   0.299814        NaN   0.135157   0.334258        NaN   \n",
       "2010-01-05   0.263810   0.306758        NaN   0.130579   0.344163        NaN   \n",
       "2010-01-06   0.268420   0.306758        NaN   0.133156   0.343206        NaN   \n",
       "2010-01-07   0.271378   0.309148        NaN   0.138866   0.346081        NaN   \n",
       "2010-01-08   0.271983   0.307050        NaN   0.137451   0.337895        NaN   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "2021-11-01   0.847529   1.076426   0.567376   0.627628   1.365001   0.039004   \n",
       "2021-11-02   0.903914   1.110001   0.582954   0.654922   1.391788   0.038585   \n",
       "2021-11-03   0.911494   1.084716   0.571102   0.648635   1.378360   0.036766   \n",
       "2021-11-04   0.919625   1.084128   0.568731   0.648635   1.378360   0.035112   \n",
       "2021-11-05   0.931619   1.098780   0.556143   0.651763   1.388310   0.036101   \n",
       "\n",
       "            000008.SZ  000009.SZ  000010.SZ  000011.SZ  ...  603989.SH  \\\n",
       "trade_date                                              ...              \n",
       "2010-01-04        NaN   0.160154   0.026386   0.114291  ...        NaN   \n",
       "2010-01-05        NaN   0.164401   0.026864   0.117950  ...        NaN   \n",
       "2010-01-06        NaN   0.164558   0.026589   0.117607  ...        NaN   \n",
       "2010-01-07        NaN   0.161496   0.027675   0.120402  ...        NaN   \n",
       "2010-01-08        NaN   0.154043   0.027192   0.119222  ...        NaN   \n",
       "...               ...        ...        ...        ...  ...        ...   \n",
       "2021-11-01   1.068719   0.146724   0.156255   0.670826  ...   0.193885   \n",
       "2021-11-02   1.083658   0.152760   0.161064   0.683667  ...   0.195271   \n",
       "2021-11-03   1.088732   0.157808   0.159834   0.679117  ...   0.190505   \n",
       "2021-11-04   1.099022   0.152760   0.160653   0.677186  ...   0.173193   \n",
       "2021-11-05   1.099022   0.163559   0.163156   0.688279  ...   0.173361   \n",
       "\n",
       "            603990.SH  603991.SH  603992.SH  603993.SH  603995.SH  603996.SH  \\\n",
       "trade_date                                                                     \n",
       "2010-01-04        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-05        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-06        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-07        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-08        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "2021-11-01   0.318522   0.108844   0.282374   0.304535   0.342689        NaN   \n",
       "2021-11-02   0.327955   0.111417   0.283728   0.312881   0.342618        NaN   \n",
       "2021-11-03   0.327279   0.111079   0.278823   0.309693   0.340483        NaN   \n",
       "2021-11-04   0.323164   0.109620   0.279705   0.322258   0.332723        NaN   \n",
       "2021-11-05   0.326286   0.109143   0.282829   0.332823   0.317723        NaN   \n",
       "\n",
       "            603997.SH  603998.SH  603999.SH  \n",
       "trade_date                                   \n",
       "2010-01-04        NaN        NaN        NaN  \n",
       "2010-01-05        NaN        NaN        NaN  \n",
       "2010-01-06        NaN        NaN        NaN  \n",
       "2010-01-07        NaN        NaN        NaN  \n",
       "2010-01-08        NaN        NaN        NaN  \n",
       "...               ...        ...        ...  \n",
       "2021-11-01   0.398073   0.527009   0.641807  \n",
       "2021-11-02   0.404907   0.532085   0.655308  \n",
       "2021-11-03   0.404531   0.529017   0.648508  \n",
       "2021-11-04   0.394384   0.528011   0.645828  \n",
       "2021-11-05   0.384394   0.529017   0.636578  \n",
       "\n",
       "[2877 rows x 1933 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "278bb00c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ed5329a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>601012.SH</th>\n",
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       "      <td>1.000000</td>\n",
       "      <td>-0.805607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BP</th>\n",
       "      <td>-0.001079</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.027304</td>\n",
       "      <td>-0.805607</td>\n",
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      "text/plain": [
       "           601012.SH  const  index_ret      SIZE        BP\n",
       "601012.SH   1.000000    NaN   0.016400  0.006968 -0.001079\n",
       "const            NaN    NaN        NaN       NaN       NaN\n",
       "index_ret   0.016400    NaN   1.000000  0.004059 -0.027304\n",
       "SIZE        0.006968    NaN   0.004059  1.000000 -0.805607\n",
       "BP         -0.001079    NaN  -0.027304 -0.805607  1.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([y,X],axis=1).corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d70ac69",
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